Early event detection (EED) systems are designed to assist the operations team by providing early warnings of approaching process upsets. EED systems use multivariate statistics and pattern recognition algorithms to monitor continuous time-series data to provide early warning of operational shifts that may drive a system into a hazardous state. The core of an EED system is a set of state estimators that embed monitoring algorithms. These algorithms use time series process data to assess the health of the monitored process. Although numerous state estimators exist, such as Principal Component Analysis (PCA), that are able to detect abnormality, the challenge lies in the translation of their output into information that is meaningful to the process operator. Currently, EED systems are designed to detect anomalies. While capable of detecting various anomalies, these applications are only able to localize some predefined failure conditions. The result is a significant dependence on manual event localization and knowledge and expertise of process operator. The automation of fault localization is a necessary element in reducing dependence on human operators.
Principal Component Analysis (PCA) is a technique of choice for many EED systems. PCA models transform a set of correlated process measurements into a set of uncorrelated variables. Most process monitoring methods look for excursions in the Q statistic, a measure of prediction error, as a means of detecting abnormal behavior. The Q statistic alone does not directly identify the source of the problem, but the individual sensor residuals are indicative of the nature of the fault. The pattern of sensor residuals can be used to more precisely identify the source of the abnormal process behavior.
An example of a fault classification by principal component analysis is discussed in U.S. Patent Application Publication No. 20050141782 by Guralnik et al which is entitled “Principal Component Analysis Based Fault Classification” and is incorporated herein by reference in its entirety. In U.S. Patent Application Publication No. 20050141782 Principal Component Analysis (PCA) is used to model a process, and clustering techniques are used to group excursions representative of events based on sensor residuals of the PCA model. The PCA model is trained on normal data, and then run on historical data that includes both normal data, and data that contains events. Bad actor data for the events can be identified by excursions in Q (residual error) and T2 (unusual variance) statistics from the normal model, resulting in a temporal sequence of bad actor vectors. Clusters of bad actor patterns that resemble one another are formed and then associated with events.
A straightforward way to represent each individual excursion as a point in N-dimensional space, where N is the number of sensors used to model the process. The contributions of each sensor to Q statistics or T2 can be expressed through weights of the vector. Unfortunately, this representation can potentially lead to poor clustering results. This is because a process is usually measured by large number of sensors, while each fault is usually caused by only a small part of the process. Therefore, if residuals of all sensors are used to represent excursions, the resulting clustering solution may be distorted by sensors unrelated to the detected faults.
One of the limitations of existing clustering approaches is that they do not take into account the closeness of data points' time of occurrence as an indication of belonging to the same event and possibly to the same cluster. These algorithms also fail to take into account special characteristics of each abnormality and noise in the data, and thus can result in incorrect decisions, which may to lead to the generation of clusters that do not represent event definitions.
It is therefore believed that a need exists for an improved method and/or system for overcoming these problems. Such methods and/or systems are discussed in greater detail herein.